{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2017 L.A. Barba, N.C. Clementi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link href=\"https://fonts.googleapis.com/css?family=Merriweather:300,300i,400,400i,700,700i,900,900i\" rel='stylesheet' >\n",
       "<link href=\"https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,300i,400,400i,700,700i\" rel='stylesheet' >\n",
       "<link href='http://fonts.googleapis.com/css?family=Source+Code+Pro:300,400' rel='stylesheet' >\n",
       "<style>\n",
       "\n",
       "@font-face {\n",
       "    font-family: \"Computer Modern\";\n",
       "    src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
       "}\n",
       "\n",
       "\n",
       "#notebook_panel { /* main background */\n",
       "    background: rgb(245,245,245);\n",
       "}\n",
       "\n",
       "div.cell { /* set cell width */\n",
       "    width: 800px;\n",
       "}\n",
       "\n",
       "div #notebook { /* centre the content */\n",
       "    background: #fff; /* white background for content */\n",
       "    width: 1000px;\n",
       "    margin: auto;\n",
       "    padding-left: 0em;\n",
       "}\n",
       "\n",
       "#notebook li { /* More space between bullet points */\n",
       "margin-top:0.5em;\n",
       "}\n",
       "\n",
       "/* draw border around running cells */\n",
       "div.cell.border-box-sizing.code_cell.running { \n",
       "    border: 1px solid #111;\n",
       "}\n",
       "\n",
       "/* Put a solid color box around each cell and its output, visually linking them*/\n",
       "div.cell.code_cell {\n",
       "    background-color: rgb(256,256,256); \n",
       "    border-radius: 0px; \n",
       "    padding: 0.5em;\n",
       "    margin-left:1em;\n",
       "    margin-top: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "div.text_cell_render{\n",
       "    font-family: 'Source Sans Pro', sans-serif;\n",
       "    line-height: 140%;\n",
       "    font-size: 110%;\n",
       "    width:680px;\n",
       "    margin-left:auto;\n",
       "    margin-right:auto;\n",
       "}\n",
       "\n",
       "/* Formatting for header cells */\n",
       ".text_cell_render h1 {\n",
       "    font-family: 'Merriweather', serif;\n",
       "    font-style:regular;\n",
       "    font-weight: bold;    \n",
       "    font-size: 250%;\n",
       "    line-height: 100%;\n",
       "    color: #004065;\n",
       "    margin-bottom: 1em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\t\n",
       ".text_cell_render h2 {\n",
       "    font-family: 'Merriweather', serif;\n",
       "    font-weight: bold; \n",
       "    font-size: 180%;\n",
       "    line-height: 100%;\n",
       "    color: #0096d6;\n",
       "    margin-bottom: 0.5em;\n",
       "    margin-top: 0.5em;\n",
       "    display: block;\n",
       "}\t\n",
       "\n",
       ".text_cell_render h3 {\n",
       "    font-family: 'Merriweather', serif;\n",
       "\tfont-size: 150%;\n",
       "    margin-top:12px;\n",
       "    margin-bottom: 3px;\n",
       "    font-style: regular;\n",
       "    color: #008367;\n",
       "}\n",
       "\n",
       ".text_cell_render h4 {    /*Use this for captions*/\n",
       "    font-family: 'Merriweather', serif;\n",
       "    font-weight: 300; \n",
       "    font-size: 100%;\n",
       "    line-height: 120%;\n",
       "    text-align: left;\n",
       "    width:500px;\n",
       "    margin-top: 1em;\n",
       "    margin-bottom: 2em;\n",
       "    margin-left: 80pt;\n",
       "    font-style: regular;\n",
       "}\n",
       "\n",
       ".text_cell_render h5 {  /*Use this for small titles*/\n",
       "    font-family: 'Source Sans Pro', sans-serif;\n",
       "    font-weight: regular;\n",
       "    font-size: 130%;\n",
       "    color: #e31937;\n",
       "    font-style: italic;\n",
       "    margin-bottom: .5em;\n",
       "    margin-top: 1em;\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 { /*use this for copyright note*/\n",
       "    font-family: 'Source Code Pro', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 9pt;\n",
       "    line-height: 100%;\n",
       "    color: grey;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\";\n",
       "\t\t\tfont-size: 90%;\n",
       "    }\n",
       "/*    .prompt{\n",
       "        display: None;\n",
       "    }*/\n",
       "\t\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }  \n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"], \n",
       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute this cell to load the notebook's style sheet, then ignore it\n",
    "from IPython.core.display import HTML\n",
    "css_file = '../style/custom.css'\n",
    "HTML(open(css_file, \"r\").read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear regression with real data\n",
    "\n",
    "## Earth temperature over time\n",
    "\n",
    "In this lesson, we will apply all that we've learned (and more) to analyze real data of Earth temperature over time.\n",
    "\n",
    "Is global temperature rising? How much? This is a question of burning importance in today's world!\n",
    "\n",
    "Data about global temperatures are available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK. Check out the [University Corporation for Atmospheric Research](https://www2.ucar.edu/climate/faq/how-much-has-global-temperature-risen-last-100-years) (UCAR) for an in-depth discussion.\n",
    "\n",
    "The [NASA Goddard Space Flight Center](http://svs.gsfc.nasa.gov/goto?3901) is one of our sources of global climate data. They produced the video below showing a color map of the changing global surface **temperature anomalies** from 1880 to 2015.\n",
    "\n",
    "The term [global temperature anomaly](https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php) means the difference in temperature with respect to a reference value or a long-term average. It is a very useful way of looking at the problem and in many ways better than absolute temperature. For example, a winter month may be colder than average in Washington DC, and also in Miami, but the absolute temperatures will be different in both places."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAUDBAoKCgoICgoJCQgJCAkJCAgJCgkICAkICAgJCAkI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\nAEIQgAQhCAP/2Q==\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/gGOzHVUQCw0\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f3ba4aead68>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('gGOzHVUQCw0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How would we go about understanding the _trends_ from the data on global temperature?\n",
    "\n",
    "The first step in analyzing unknown data is to generate some simple plots using **Matplotlib**. We are going to look at the temperature-anomaly history, contained in a file, and make our first plot to explore this data. \n",
    "\n",
    "We are going to smooth the data and then we'll fit a line to it to find a trend, plotting along the way to see how it all looks.\n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Read a data file\n",
    "\n",
    "We took the data from the [NOAA](https://www.ncdc.noaa.gov/cag/) (National Oceanic and Atmospheric Administration) webpage. Feel free to play around with the webpage and analyze data on your own, but for now, let's make sure we're working with the same dataset.\n",
    "\n",
    "\n",
    "We have a file named `land_global_temperature_anomaly-1880-2016.csv` in our `data` folder. This file contains the year on the first column, and averages of land temperature anomaly listed sequentially on the second column, from the year 1880 to 2016. We will load the file, then make an initial plot to see what it looks like.\n",
    "\n",
    "\n",
    "##### Note:\n",
    "\n",
    "If you downloaded this notebook alone, rather than the full collection for this course, you may not have the data file on the location we assume below. In that case, you can download the data if you add a code cell, and execute the following code in it:\n",
    "\n",
    "```Python\n",
    "from urllib.request import urlretrieve\n",
    "URL = 'http://go.gwu.edu/engcomp1data5?accessType=DOWNLOAD'\n",
    "urlretrieve(URL, 'land_global_temperature_anomaly-1880-2016.csv')\n",
    "```\n",
    "The data file will be downloaded to your working directory, and you will then need to remove the path information, i.e., the string `'../../data/'`, from the definition of the variable `fname` below.\n",
    "\n",
    "Let's start by importing NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load our data from the file, we'll use the function [`numpy.loadtxt()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html), which lets us immediately save the data into NumPy arrays. (We encourage you to read the documentation for details on how the function works.) Here, we'll save the data into the arrays `year` and `temp_anomaly`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = 'data/land_global_temperature_anomaly-1880-2016.csv'\n",
    "\n",
    "year, temp_anomaly = numpy.loadtxt(fname, delimiter=',', skiprows=5, unpack=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise\n",
    "\n",
    "Inspect the data by printing `year` and `temp_anomaly`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Plot the data\n",
    "\n",
    "Let's first load the **Matplotlib** module called `pyplot`, for making 2D plots. Remember that to get the plots inside the notebook, we use a special \"magic\" command, `%matplotlib inline`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `plot()` function of the `pyplot` module makes simple line plots. We avoid that stuff that appeared on top of the figure, that `Out[x]: [< ...>]` ugliness, by adding a semicolon at the end of the plotting command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEACAYAAAC3adEgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRWdiNsihAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b8de5ae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(year, temp_anomaly);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we have a line plot, but if you see this plot without any information you would not be able to figure out what kind of data it is! We need labels on the axes, a title and why not a better color, font and size of the ticks. \n",
    "**Publication quality** plots should always be your standard for plotting. \n",
    "How you present your data will allow others (and probably you in the future) to better understand your work. \n",
    "\n",
    "We can customize the style of our plots using **Matplotlib**'s [`rcParams`](https://matplotlib.org/api/matplotlib_configuration_api.html#matplotlib.rcParams). It lets us set some style options that apply for all the plots we create in the current session.\n",
    "Here, we'll make the font of a specific size and type. You can also customize other parameters like line width, color, and so on (check out the documentation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['font.family'] = 'serif'\n",
    "rcParams['font.size'] = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll redo the same plot, but now we'll add a few things to make it prettier and **publication quality**. We'll add a title, label the axes and, show a background grid. Study the commands below and looks at the result!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApEAAAF4CAYAAAAMiKIcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX1AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b7fae7c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#You can set the size of the figure by doing:\n",
    "pyplot.figure(figsize=(10,5))\n",
    "\n",
    "#Plotting\n",
    "pyplot.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1) \n",
    "pyplot.title('Land global temperature anomalies. \\n')\n",
    "pyplot.xlabel('Year')\n",
    "pyplot.ylabel('Land temperature anomaly [°C]')\n",
    "pyplot.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Better, no? Feel free to play around with the parameters and see how the plot changes. There's nothing like trial and error to get the hang of it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Least-squares linear regression \n",
    "\n",
    "In order to have an idea of the general behavior of our data, we can find a smooth curve that (approximately) fits the points. We generally look for a curve that's simple (e.g., a polynomial), and does not reproduce the noise that's always present in experimental data. \n",
    "\n",
    "Let $f(x)$ be the function that we'll fit to the $n+1$ data points: $(x_i, y_i)$, $i = 0, 1, ... ,n$:\n",
    "\n",
    "$$ \n",
    "    f(x) = f(x; a_0, a_1, ... , a_m) \n",
    "$$\n",
    "\n",
    "The notation above means that $f$ is a function of $x$, with $m+1$ variable parameters $a_0, a_1, ... , a_m$, where $m < n$. We need to choose the form of $f(x)$ a priori, by inspecting the experimental data and knowing something about the phenomenon we've measured. Thus, curve fitting consists of two steps: \n",
    "\n",
    "1. Choosing the form of $f(x)$.\n",
    "2. Computing the parameters that will give us the \"best fit\" to the data. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the \"best\" fit?\n",
    "\n",
    "When the noise in the data is limited to the $y$-coordinate, it's common to use a **least-squares fit**, which minimizes the function\n",
    "\n",
    "$$\n",
    "\\begin{equation}    \n",
    "    S(a_0, a_1, ... , a_m) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2\n",
    "\\end{equation}    \n",
    "$$\n",
    "\n",
    "with respect to each $a_j$. We find the values of the parameters for the best fit by solving the following equations:\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\frac{\\partial{S}}{\\partial{a_k}} = 0, \\quad k = 0, 1, ... , m.\n",
    "\\end{equation}\n",
    "$$\n",
    "\n",
    "Here, the terms $r_i = y_i - f(x_i)$ are called residuals: they tell us the discrepancy between the data and the fitting function at $x_i$. \n",
    "\n",
    "Take a look at the function $S$: what we want to minimize is the sum of the squares of the residuals. The equations (2) are generally nonlinear in $a_j$ and might be difficult to solve. Therefore, the fitting function is commonly chosen as a linear combination of specified functions $f_j(x)$, \n",
    "\n",
    "$$\n",
    "\\begin{equation*}\n",
    "    f(x) = a_0f_0(x) + a_1f_1(x) + ... + a_mf_m(x)\n",
    "\\end{equation*}\n",
    "$$\n",
    "\n",
    "which results in equations (2) being linear. In the case that the fitting function is polynomial, we have have $f_0(x) = 1, \\; f_1(x) = x, \\; f_2(x) = x^2$, and so on.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression \n",
    "\n",
    "When we talk about linear regression we mean \"fitting a straight line to the data.\" Thus,\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    f(x) = a_0 + a_1x\n",
    "\\end{equation}\n",
    "$$\n",
    "\n",
    "In this case, the function that we'll minimize is:\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    S(a_0, a_1) = \\sum_{i=0}^{n} [y_i - f(x_i)]^2 = \\sum_{i=0}^{n} (y_i - a_0 - a_1x_i)^2 \n",
    "\\end{equation}    \n",
    "$$\n",
    "\n",
    "Equations (2) become:\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\frac{\\partial{S}}{\\partial{a_0}} =  \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i) = 2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] = 0\n",
    "\\end{equation}    \n",
    "$$\n",
    "\n",
    "$$\n",
    "\\begin{equation}\n",
    "    \\frac{\\partial{S}}{\\partial{a_1}} =  \\sum_{i=0}^{n} -2(y_i - a_0 - a_1x_i)x_i = 2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] = 0\n",
    "\\end{equation}    \n",
    "$$\n",
    "\n",
    "Let's divide both equations by $2(n+1)$ and rearrange terms.\n",
    "\n",
    "Rearranging (6) and (7):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    2 \\left[ a_0(n+1) + a_1\\sum_{i=0}^{n} x_i - \\sum_{i=0}^{n} y_i \\right] &= 0 \\nonumber \\\\ \n",
    "    \\frac{a_0(n+1)}{n+1} + a_1 \\frac{\\sum_{i=0}^{n} x_i}{n+1} - \\frac{\\sum_{i=0}^{n} y_i}{n+1} &= 0 \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    a_0  = \\bar{y} - a_1\\bar{x}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $\\bar{x} = \\frac{\\sum_{i=0}^{n} x_i}{n+1}$ and $\\bar{y} = \\frac{\\sum_{i=0}^{n} y_i}{n+1}$.\n",
    "\n",
    "Rearranging (7):\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    2 \\left[ a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i \\right] &= 0 \\\\\n",
    "    a_0\\sum_{i=0}^{n} x_i + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i &=0 \\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Now, if we replace $a_0$ from equation (8) into (9) and rearrange terms:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "    (\\bar{y} - a_1\\bar{x})\\sum_{i=0}^{n} x_i  + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i  &= 0 \\\\ \n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Replacing the definitions of the mean values into the equation,  \n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "    \\left[\\frac{1}{n+1}\\sum_{i=0}^{n} y_i  - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\right]\\sum_{i=0}^{n} x_i  + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i  &= 0  \\\\ \n",
    "     \\frac{1}{n+1}\\sum_{i=0}^{n} y_i \\sum_{i=0}^{n} x_i - \\frac{a_1}{n+1}\\sum_{i=0}^{n} x_i \\sum_{i=0}^{n} x_i  + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i  &= 0  \\\\ \n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Leaving everything in terms of $\\bar{x}$, \n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "    \\sum_{i=0}^{n} y_i \\bar{x} - a_1\\sum_{i=0}^{n} x_i \\bar{x} + a_1\\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_iy_i  = 0  \n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Grouping the terms that have $a_1$ on the left-hand side and the rest on the right-hand side:\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "    a_1\\left[ \\sum_{i=0}^{n} x_{i}^2 - \\sum_{i=0}^{n} x_i \\bar{x}\\right] &= \\sum_{i=0}^{n} x_iy_i - \\sum_{i=0}^{n} y_i \\bar{x} \\\\\n",
    "    a_1 \\sum_{i=0}^{n} (x_{i}^2 - x_i \\bar{x}) &= \\sum_{i=0}^{n} (x_iy_i -  y_i \\bar{x}) \\\\\n",
    "    a_1 \\sum_{i=0}^{n} x_{i}(x_{i} -\\bar{x}) &= \\sum_{i=0}^{n} y_i(x_i - \\bar{x})    \n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "Finally, we get that:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})}\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Then our coefficients are:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0  = \\bar{y} - a_1\\bar{x}\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's fit!\n",
    "\n",
    "Let's now fit a straight line through the temperature-anomaly data, to see the trend over time. We'll use least-squares linear regression to find the slope and intercept of a line \n",
    "\n",
    "$$y = a_1x+a_0$$\n",
    "\n",
    "that fits our data.\n",
    "\n",
    "In our case, the `x`-data corresponds to `year`, and the `y`-data is `temp_anomaly`. To calculate our coefficients with the formula above, we need the mean values of our data. Sine we'll need to compute the mean for both `x` and `y`, it could be useful to write a custom Python _function_ that computes the mean for any array, and we can then reuse it.\n",
    "\n",
    "It is good coding practice to *avoid repeating* ourselves: we want to write code that is reusable, not only because it leads to less typing but also because it reduces errors. If you find yourself doing the same calculation multiple times, it's better to encapsulate it into a *function*. \n",
    "\n",
    "Remember the _key concept_ from [Lesson 1](http://go.gwu.edu/engcomp1lesson1): A function is a compact collection of code that executes some action on its arguments. \n",
    "\n",
    "Once *defined*, you can *call* a function as many times as you want. When we *call* a function, we execute all the code inside the function. The result of the execution depends on the *definition* of the function and on the values that are *passed* into it as *arguments*. Functions might or might not *return* values in their last operation.   \n",
    "\n",
    "The syntax for defining custom Python functions is:\n",
    "\n",
    "```python\n",
    "def function_name(arg_1, arg_2, ...):\n",
    "    '''\n",
    "    docstring: description of the function\n",
    "    '''\n",
    "    <body of the function>\n",
    "```\n",
    "\n",
    "The **docstring** of a function is a message from the programmer documenting what he or she built. Docstrings should be descriptive and concise. They are important because they explain (or remind) the intended use of the function to the users. You can later access the docstring of a function using the function `help()` and passing the name of the function. If you are in a notebook, you can also prepend a question mark `'?'` before the name of the function and run the cell to display the information of a function. \n",
    "\n",
    "Try it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "?print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `help` function instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function print in module builtins:\n",
      "\n",
      "print(...)\n",
      "    print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n",
      "    \n",
      "    Prints the values to a stream, or to sys.stdout by default.\n",
      "    Optional keyword arguments:\n",
      "    file:  a file-like object (stream); defaults to the current sys.stdout.\n",
      "    sep:   string inserted between values, default a space.\n",
      "    end:   string appended after the last value, default a newline.\n",
      "    flush: whether to forcibly flush the stream.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(print)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define a custom function that calculates the mean value of any array. Study the code below carefully. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_value(array):\n",
    "    \"\"\" Calculate the mean value of an array \n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    array: Numpy array \n",
    "    \n",
    "    Returns\n",
    "    -------    \n",
    "    mean: mean value of the array\n",
    "    \"\"\"\n",
    "    sum_elem = 0\n",
    "    for element in array:\n",
    "            sum_elem += element # this is the same as sum_elem = sum_elem + element\n",
    "    \n",
    "    mean = sum_elem / len(array)\n",
    "    \n",
    "    return mean\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you execute the cell above, the function`mean_value()` becomes available to use on any argument of the correct type. This function works on arrays of any length. We can try it now with our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1948.0\n"
     ]
    }
   ],
   "source": [
    "year_mean = mean_value(year)\n",
    "print(year_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0526277372263\n"
     ]
    }
   ],
   "source": [
    "temp_anomaly_mean = mean_value(temp_anomaly)\n",
    "print(temp_anomaly_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neat! You learned how to write a Python function, and we wrote one for computing the mean value of an array of numbers. We didn't have to, though, because NumPy has a built-in function to do just what we needed: [`numpy.mean()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.mean.html).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise \n",
    "\n",
    "Calculate the mean of the `year` and `temp_anomaly` arrays using the NumPy built-in function, and compare the results with the ones obtained using our custom `mean_value` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have mean values, we can compute our coefficients by following equations (12). We first calculate $a_1$ and then use that value to calculate $a_0$.\n",
    "\n",
    "Our coefficients are:\n",
    "\n",
    "$$\n",
    "    a_1 = \\frac{ \\sum_{i=0}^{n} y_{i} (x_i - \\bar{x})}{\\sum_{i=0}^{n} x_i (x_i - \\bar{x})} \\quad , \\quad a_0  = \\bar{y} - a_1\\bar{x}\n",
    "$$ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We already calculated the mean values of the data arrays, but the formula requires two sums over new derived arrays. Guess what, NumPy has a built-in function for that: [`numpy.sum()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html). Study the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_1 = numpy.sum(temp_anomaly*(year - year_mean)) / numpy.sum(year*(year - year_mean)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0103702839435\n"
     ]
    }
   ],
   "source": [
    "print(a_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_0 = temp_anomaly_mean - a_1*year_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-20.1486853847\n"
     ]
    }
   ],
   "source": [
    "print(a_0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise\n",
    "\n",
    "Write a function that computes the coefficients, call the function to compute them and compare the result with the values we obtained before. As a hint, we give you the structure that you should follow:\n",
    "\n",
    "```python\n",
    "def coefficients(x, y, x_mean, y_mean):\n",
    "    \"\"\"\n",
    "    Write docstrings here\n",
    "    \"\"\"\n",
    "\n",
    "    a_1 = \n",
    "    a_0 = \n",
    "    \n",
    "    return a_1, a_0\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have the coefficients of a linear function that best fits our data. With them, we can compute the predicted values of temperature anomaly, according to our fit. Check again the equations above: the values we are going to compute are $f(x_i)$. \n",
    "\n",
    "Let's call `reg` the array obtined from evaluating $f(x_i)$ for all years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = a_0 + a_1 * year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the values of our linear regression, we can plot it on top of the original data to see how they look together. Study the code below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApEAAAFYCAYAAAALJKcqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzOrzUWkmZmZmdX2P9QW+8mLRcVuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b7fa34828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(10, 5))\n",
    "\n",
    "pyplot.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n",
    "pyplot.plot(year, reg, 'k--', linewidth=2, label='Linear regression')\n",
    "pyplot.xlabel('Year')\n",
    "pyplot.ylabel('Land temperature anomaly [°C]')\n",
    "pyplot.legend(loc='best', fontsize=15)\n",
    "pyplot.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Apply regression using NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we coded linear regression from scratch. But, guess what: we didn't have to because NumPy has built-in functions that do what we need!\n",
    "\n",
    "Yes! Python and NumPy are here to help! With [`polyfit()`](https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.polyfit.html), we get the slope and $y$-intercept of the line that best fits the data. With [`poly1d()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.poly1d.html), we can build the linear function from its slope and $y$-intercept.\n",
    "\n",
    "Check it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First fit with NumPy, then name the coefficients obtained a_1n, a_0n:\n",
    "a_1n, a_0n = numpy.polyfit(year, temp_anomaly, 1)\n",
    "\n",
    "f_linear = numpy.poly1d((a_1n, a_0n)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0103702839435\n"
     ]
    }
   ],
   "source": [
    "print(a_1n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-20.1486853847\n"
     ]
    }
   ],
   "source": [
    "print(a_0n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "0.01037 x - 20.15\n"
     ]
    }
   ],
   "source": [
    "print(f_linear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApEAAAFYCAYAAAALJKcqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzOrzUWkmZmZmdX2P9QW+8mLRcVuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b7fa0d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(10, 5))\n",
    "\n",
    "pyplot.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n",
    "pyplot.plot(year, f_linear(year), 'k--', linewidth=2, label='Linear regression')\n",
    "pyplot.xlabel('Year')\n",
    "pyplot.ylabel('Land temperature anomaly [°C]')\n",
    "pyplot.legend(loc='best', fontsize=15)\n",
    "pyplot.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Split regression\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you look at the plot above, you might notice that around 1970 the temperature starts increasing faster that the previous trend. So maybe one single straight line does not give us a good-enough fit.\n",
    "\n",
    "What if we break the data in two (before and after 1970) and do a linear regression in each segment? \n",
    "\n",
    "To do that, we first need to find the position in our `year` array where the year 1970 is located. Thankfully, NumPy has a function called  [`numpy.where()`](https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) that can help us. We pass a condition and `numpy.where()` tells us where in the array the condition is `True`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([90]),)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.where(year==1970)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To split the data, we use the powerful instrument of _slicing_ with the colon notation. Remember that a colon between two indices indicates a range of values from a `start` to an `end`. The rule is that `[start:end]` includes the element at index `start` but excludes the one at index `end`. For example, to grab the first 3 years in our `year` array, we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1880.,  1881.,  1882.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we know how to split our data in two sets, to get two regression lines. We need two slices of the arrays `year` and `temp_anomaly`, which we'll save in new variable names below. After that, we complete two linear fits using the helpful NumPy functions we learned above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_1 , temp_anomaly_1 = year[0:90], temp_anomaly[0:90]\n",
    "year_2 , temp_anomaly_2 = year[90:], temp_anomaly[90:]\n",
    "\n",
    "m1, b1 = numpy.polyfit(year_1, temp_anomaly_1, 1)\n",
    "m2, b2 = numpy.polyfit(year_2, temp_anomaly_2, 1)\n",
    "\n",
    "f_linear_1 = numpy.poly1d((m1, b1))\n",
    "f_linear_2 = numpy.poly1d((m2, b2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApEAAAFYCAYAAAALJKcqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxpimsRNpjDHGGGOaxk6kMcYYY4xpmv8HD8EHSH7PPwoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b7f8e1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(10, 5))\n",
    "\n",
    "pyplot.plot(year, temp_anomaly, color='#2929a3', linestyle='-', linewidth=1, alpha=0.5) \n",
    "pyplot.plot(year_1, f_linear_1(year_1), 'g--', linewidth=2, label='1880-1969')\n",
    "pyplot.plot(year_2, f_linear_2(year_2), 'r--', linewidth=2, label='1970-2016')\n",
    "\n",
    "pyplot.xlabel('Year')\n",
    "pyplot.ylabel('Land temperature anomaly [°C]')\n",
    "pyplot.legend(loc='best', fontsize=15)\n",
    "pyplot.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have two different curves for two different parts of our data set. A little problem with this and is that the end point of our first regression doesn't match the starting point of the second regression. We did this for the purpose of learning, but it is not rigorously correct. We'll fix in in the next course module when we learn more about different types of regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We learned:\n",
    "\n",
    "* Making our plots more beautiful\n",
    "* Defining and calling custom Python functions\n",
    "* Applying linear regression to data\n",
    "* NumPy built-ins for linear regression\n",
    "* The Earth is warming up!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. [_Essential skills for reproducible research computing_](https://barbagroup.github.io/essential_skills_RRC/) (2017). Lorena A. Barba,  Natalia C. Clementi, Gilbert Forsyth. \n",
    "2. _Numerical Methods in Engineering with Python 3_ (2013). Jaan Kiusalaas. Cambridge University Press.\n",
    "3. _Effective Computation in Physics: Field Guide to Research with Python_ (2015). Anthony Scopatz & Kathryn D. Huff. O'Reilly Media, Inc.\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
